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This study developed a multilayered, gel-based, and underivatized strategy for  de novo  protein sequence analysis of unsequenced dinoflagellates using a MALDI-TOF/TOF mass spectrometer with the assistance of DeNovo Explorer software. MASCOT was applied as the first layer screen to identify either known or unknown proteins sharing identical peptides presented in a database. Once the confident identifications were removed after searching against the NCBInr database, the remainder was searched against the dinoflagellate expressed sequence tag database. In the last layer, those borderline and nonconfident hits were further subjected to  de novo  interpretation using DeNovo Explorer software. The  de novo  sequences passing a reliability filter were subsequently submitted to nonredundant MS-BLAST search. Using this layer identification method, 216 protein spots representing 158 unique proteins out of 220 selected protein spots from  Alexandrium tamarense , a dinoflagellate with unsequenced genome, were confidently or tentatively identified by database searching. These proteins were involved in various intracellular physiological activities. This study is the first effort to develop a completely automated approach to identify proteins from unsequenced dinoflagellate databases and establishes a preliminary protein database for various physiological studies of dinoflagellates in the future.

Homology-Driven Proteomics of Dinoflagellates with Unsequenced Genomes Using MALDI-TOF/TOF and AutomatedDe NovoSequencing